黄仁勋：英伟达的未来、物理 AI、智能体崛起与推理爆炸

Jason Calacanis (00:00:00): Special episode this week. We've preempted the weekly show, and there's only three people we preempt the show for, president Trump, Jesus, and Jensen. And I'll let you pick which order we do that. But what an amazing run you've had and and a great event. Jensen Huang (00:00:19): Every industry is here. Every tech company is here. Every AI company is here. Incredible. Incredible. If you Jason Calacanis (00:00:27): were building a global financial system from first principles today, you wouldn't build it on 50 year old legacy rails. You'd build Airwallex, one AI native platform for global accounts, cards, and payments. It's designed to make the entire world feel like a local market. Others are bolting AI onto broken infrastructure, but Airwallex was built for the intelligent era from day one. Stop paying the legacy tax and start building the future at airwallex.com slash all in. Airwallex, build the future. And one of the great announcements of the past year has been Grok. When you made the purchase of Grok, did you realize how insufferable chaff Chamath would become? Jensen Huang (00:01:10): I had a I had an inkling that that Jason Calacanis (00:01:13): that We're his friends. Chamath Palihapitiya (00:01:14): We have to deal with them every week. Jensen Huang (00:01:16): I know it. Jason Calacanis (00:01:16): You had to deal with them for the six week close. Jensen Huang (00:01:19): I know it. Chamath Palihapitiya (00:01:19): It's like two weeks. Jensen Huang (00:01:20): Two weeks. It's all coming back to me now. It's it's making me rather uncomfortable. The the thing is many of our strategies are are presented in in broad daylight at GTC years in advance of when we do it. Two and a half years ago, I introduced the operating system of the AI factory, and it's called Dynamo. Dynamo, as you know, is a piece of instrument, a machine that was created by Siemens to turn essentially water into electricity. And Dynamo, powered the factory of the last industrial revolution. So I thought it was the perfect name for the operating system of the next industrial revolution, the factory of that. And so inside Dynamo, the fundamental technology is disaggregated inference. Jason, I I know you're you're you're super technical. Absolutely. I know it. Jason Calacanis (00:02:15): I'll let you take this one. Go ahead and define it for the audience. I don't wanna snap on you. Jensen Huang (00:02:19): Yeah. Thank you. I I I knew you wanted to jump in there for a second. It's it's disaggregated inference, which means the processing pipeline of inference is extremely complicated. In fact, it is the most complicated computing problem today. Incredible scale, lots of mathematics of different shapes and sizes. And we came up with the idea that you would change, you would disaggregate parts of the processing such that some of it can run on some GPUs, rest of it can run on different GPUs. And that led to us realizing that maybe even disaggregated computing could make sense, that we could have different heterogeneous nature of computing. That same sensibility led us to melonize. Yep. You know, today, NVIDIA's computing is spread across GPUs, CPUs, switches, scale up switches, scale out switches, networking processors, and now we're gonna add Grok to that, and we're gonna put the right workload on the right chips. You know, we just really evolved from a GPU company to an AI factory company. Chamath Palihapitiya (00:03:24): I mean, I think that was probably the biggest takeaway that I had. You're seeing this fundamental disaggregation where we've gone from a GPU, and now you have this complexion of all these different options that will eventually exist. The thing that you guys said on stage or you said on stage was, I I would like the high value inference people to take a listen to this, and 25% of your data center space, you said, should be allocated to this Grok LPU GPU combo. Jensen Huang (00:03:50): Grok to about 25% of the Vera Rubins in the g in the data center. Chamath Palihapitiya (00:03:55): So can you tell us about how the industry looks at this idea of now basically creating this next generation form of disaggregated prefilled, decode, disag Mhmm. And how people, do think, will react to it? Jensen Huang (00:04:06): Yeah. And take a step back. At the time that we added this, we went from large language model processing to agentic processing. Now, when you're running an agent, you're accessing working memory. You're accessing long term memory. You're using tools. You're really beating up on storage really hard. You have agents working with other agents. Some of the agents are very large models. Some of them are smaller models. Some of them are diffusion models. Some of them are autoregressive models. And so there are all kinds of different types of models inside this data center. We created Vera Rubin to be able to run this extraordinarily diverse workload. My sense is and and so we added what used to be a one rack company. We now added four more racks. Right. So NVIDIA's TAM, if you will, increased from whatever whatever it was to probably something, call it, you know, 33%, 50% higher. Now part of that 33% or 50%, a lot of it's gonna be storage processors. It's called BlueField. Some of it will be a lot of it, I'm hoping, will be Grok processors, and some of it will be CPUs. And they're all gonna and a lot of it's gonna be networking processors. And so all of this is gonna be running basically the computer of the AI revolution called agents. Chamath Palihapitiya (00:05:30): Right. Jensen Huang (00:05:30): The operating system of of What about modern modern industry. David Friedberg (00:05:34): What about embedded applications? So, you know, my daughter's teddy bear at home wants to talk to her. What goes in there? Is it a custom ASIC, or does there end up becoming much more kind of a broader set of TAM with developing tools that are maybe different for different use cases at the edge and in an embedded application set? Jensen Huang (00:05:52): That there's three computers in the problem at the largest scale when you take a step back. There's one computer that's really about training the AI model, developing, creating the AI. Another computer for evaluating it. Depending on the type of problem you're having, like, for example, you look around, there's all kinds of robots and cars and things like that, you have to evaluate these robots inside a virtual gym that represents the physical world. So it has to be software that obeys the laws of physics. And that's a second computer. We call that omniverse. The third computer is the computer at the edge, the robotics computer. That robotics computer, one of them could be self driving car. Another one's a robot. Another one could be a teddy bear. Little tiny one for a teddy bear. One of the most important ones is the one that we're working on that basically turns the telecommunications base stations into part of the AI infrastructure. So now all of the it's a $2,000,000,000,000 industry. All of that, in time, will be transformed into an extension of the AI infrastructure. And so radios radios will become edge devices. Factories, warehouses, you name it. And so so there are three these three basic computers, all of them, you know, aren't gonna be necessary. Brad Gerstner (00:07:11): Jensen, last last year, I think you were ahead of the the rest of the world in in in saying inference isn't gonna a thousand x. Jason Calacanis (00:07:18): Just last year. Brad Gerstner (00:07:19): Yes. Brad, you hurt my feelings. Gonna Is 1,000,000 x? It's gonna 1,000,000,000 x. Jensen Huang (00:07:24): Yeah. Brad Gerstner (00:07:25): Right? And I think people at the time thought it was pretty hyperbolic because the world was still focused on prescaling, on training. Here we are. Now inference has exploded. We're inference constrained. You announced an inference factory that I think is leading edge that's gonna be 10x better in terms of throughput to the next factory, but yet if I listen to what the chatter is out there, it's that your inference is gonna cost 40 or 50,000,000,000, and the alternatives, the custom ASICs, AMD, others, are gonna cost 25 to 30,000,000,000, and you're gonna lose share. So why don't you talk to us? What are you seeing? How do you think about share? And does it make sense for all these folks to pay something that's a two x premium to what others are marketing? Jensen Huang (00:08:07): The big takeaway, the big idea is that you should not equate the price of the factory and the price of the tokens, the cost of the tokens. It is very likely that the $50,000,000,000 factory and in fact, I can prove it that the $50,000,000,000 factory will generate for you the lowest cost tokens. And the reason for that is because we produce these tokens at extraordinary efficiency. 10 times you know, the difference between 50,000,000,000. Now it turns out 20,000,000,000 is just LAN power and shell. Right? Brad Gerstner (00:08:47): Right. Jensen Huang (00:08:47): And then on top of that, you have storage anyways, networking anyways, you got CPUs anyways, you got servers anyways, you got cooling anyways. The difference between that GPU being one x price or half x price Chamath Palihapitiya (00:08:59): Right. Jensen Huang (00:09:00): Is not between 50,000,000,000 and 30,000,000,000. Right. Pick your favorite number, but let's say between 50,000,000,000 and 40,000,000,000. Jason Calacanis (00:09:07): K. Jensen Huang (00:09:07): That is not a large percentage when the $50,000,000,000 data center is actually 10 times the throughput. Right. Jason Calacanis (00:09:15): Jesse, I Jensen Huang (00:09:15): wanna That's the reason why I said that even for most chips, if you can't keep up with the state of the technology and the pace that we're running, even when the chips are free, it's not cheap enough. Chamath Palihapitiya (00:09:26): Yeah. Yeah. Can I can I just ask a general strategy question? Jensen Huang (00:09:29): Yep. Chamath Palihapitiya (00:09:30): I mean, you're running the most valuable company in the world. This thing is gonna do 350 plus billion of revenue next year, 200,000,000,000 of free cash flow. It's compounding at these crazy rates. How do you decide what to do? Like, how do you actually get the information? I mean, it's famous now, these sort of emails that are people are meant to send you. But how do you really decide to get an intuition of how to shape the market, where to really double down, where to maybe pull back, where to actually go into a greenfield? How how does that information get to you? How do you decide these things? Jensen Huang (00:10:01): In a final analysis, that's the job of the CEO. Yeah. And our job is to define the strategy, define the vision, define the strategy. We're informed, of course, by amazing computer scientists, amazing technologists, great people all over the company, but we have to shape that future. Well, part of it has to do with, is this something that's insanely hard to do? If it's not hard to do, we should back away from it. And the reason for that, if if it's easy to do, obviously Lots of competitors. A lot of competitors. Chamath Palihapitiya (00:10:28): Yeah. Jensen Huang (00:10:28): Is this something that has never been done before that's insanely hard to do, and that somehow taps into the special superpowers of our company. And so I have to find this confluence of things to that meets the standard. And in the end, we also know that a lot of pain and suffering is gonna go into Yeah. There are no great things that are invented because it was just easy to do, just like first try, here we are. And so if it's super hard to do, nobody's ever done it before, it's very likely that you're gonna have a lot of pain and suffering. Jason Calacanis (00:10:59): Can you Jensen Huang (00:10:59): And so you better enjoy it. Chamath Palihapitiya (00:11:01): So can you can you just look at maybe three or four of the more long tail things you announced and just talk about the long term viability of whether it's the data centers in space or whether it's what you're trying to do with ADAS and autos or, you know, what you're trying to do on the biology side. Just give us a sense of, like, how you see some of these curves inflecting upwards in some of these longer tail businesses. Jensen Huang (00:11:20): Excellent. Physical AI, large category. We believe, and I just mentioned, we have three computing systems, all the software platforms on top of it. Physical AI as a large category, it's technology industry's first opportunity to address a $50,000,000,000,000 industry that has largely been, you know, void of technology until now. And so we need to invent all of the technology necessary to do that. I felt that that was a ten year journey. We started ten years ago. We're seeing it inflecting now. It is a multibillion dollar business for us. It's close to $10,000,000,000 a year now, and so it's a big business, and it's growing exponentially. And so that's number one. I think in the case of digital biology, I think we are literally near the ChatGPT moment of digital biology. We're about to understand how to represent genes, proteins, cells. We already know how to understand chemicals. And so the ability for us to represent and understand the dynamics of the building blocks of biology, that's a couple, two, three, five years from now. In five years' time, I completely believe that the health care industry or digital biology is gonna inflect. And so these are a couple of the really great ones, and you could see they're all around us. David Friedberg (00:12:36): Agriculture. Agriculture. Jensen Huang (00:12:38): Inflecting now. No question. Yeah. Jason Calacanis (00:12:40): Jensen, I wanna take you from the data center to the desktop. The company was built in large part on hobbyists, video gamers, and and all those graphic cards in the beginning. And you mentioned in front of, I think, 10,000 people here, just clawed OpenClaw, clawed code, and what a revolution agents have become. And specifically, the hobbyists who are really where a lot of energy we see, you know, a lot of the innovation breaks want desktops. You announced one here. I believe it's the Dell 6,800. This is a very powerful workstation to run local models, 750 gigs of RAM. Obviously, the the Mac, studio sold out everywhere. In my company, we're moving to OpenClaw everything. Freebird just got claw pilled. You got claw pilled. I understand that you're obsessed with these. What is this from the streets movement of creating open source agents and using open source on the desktop mean to you? Jensen Huang (00:13:40): So great. Jason Calacanis (00:13:40): Where is that going? Jensen Huang (00:13:41): Yeah. So great. First of all, let's take a step back. In the last two years, we saw basically three inflection points. The first one was generative. ChatGPT brought AI to the common everybody, to our awareness. But the fact of the matter is the technology sat in plain sight months before GPT. It wasn't until ChatGPT put a user interface around it, made it easy for us to use, that generative AI took off. Now generative AI, as you know, generates tokens for internal consumption as well as external consumption. Yeah. Internal consumption is thinking, which led to reasoning. O one and o three continued that wave of chat GBT grounded information, made AI not only answer questions, but answer questions in a more grounded way useful. We started seeing the revenues and the economic model of OpenAI start to inflect. Then the third one was only inside the industry that we saw, Clog Code, the first agentic system that was very useful. Really revolutionary stuff. But on but Clog code was only available for enterprises. Most people outside never saw anything about Clog code until OpenClaw. OpenCLaw basically put into the popular consciousness what an AI agent can do. That's the reason why OpenCLaw is so important from a cultural perspective. Now, the second reason why it's so important is that OpenCLaw is open, but it formulates, it structures a type of computing model that is basically reinventing computing altogether. It has a memory system. Scratch is a short term memory file system. Has Skills. It has it has scales. Did you say skills or scales? Skills. Oh, skills. Jason Calacanis (00:15:37): If have scales Yeah, so Theoretically, yeah. David Friedberg (00:15:39): Yeah, skills. Jensen Huang (00:15:40): So the first thing, first thing, it has resources. It manages resources. Does scheduling. Yep. Right? And it cron jobs. It could spawn off agents. It could decompose a task and solve problems, it does scheduling. It has IO subsystems. It could input. It has output. It connect to WhatsApp. And also, it has a API that allows it to run multiple types of applications called skills. Yeah. These four elements fundamentally define a computer. Chamath Palihapitiya (00:16:13): Yeah. Jensen Huang (00:16:14): And therefore, what do we have? We have a personal artificial intelligence computer for the very first time. Open source. It's open source. It runs literally everywhere. And so this is basically the blueprint, the operating system of modern computing. Jason Calacanis (00:16:33): Yeah. Jensen Huang (00:16:34): And it's gonna run literally everywhere. Now, of course, one of the things that we had to help it do is whenever you have AgenTx software, you have to make sure that an AgenTx software has access to sensitive information. It can execute code. It can communicate externally. We have to make sure that all of it has to be governed, all of it has to be secured, and that we have policies that gives these agents two of the three things, but not all three things at the same time. And so the governance part of it, we contributed to Peter. Peter Steinberger was here, so we've got a mound of great engineers working with him to help secure and keep that thing so that it could protect our privacy, protect our security. David Friedberg (00:17:12): Jensen, that paradigm shift makes some of the AI legislation that has passed around the country to regulate AI and a lot of the proposed legislation effectively moot, doesn't it? Can you just comment for a second on how quickly the paradigm shift kind of obviates a lot of the models for regulatory oversight of AI, which is becoming a very hot topic in politics right now? Jensen Huang (00:17:34): Well, this is this is the part that that we just with policymakers, we need to we need to always get in front of them. And, Brad, you do a great job doing this. We had to get in front of them and inform them about the state of the technology, what it is, what it is not. Mhmm. It is not a biological being. It is not alien. It is not conscious. It is computer software. David Friedberg (00:18:00): Yeah. Exactly. Jensen Huang (00:18:01): And and it is not something that we say things like, we don't understand it at all. Right. It is not true. We don't understand it all. We understand a lot of things about this technology. And and so so I think, one, we have to make sure that we continue to inform the policymakers and not affect not allow doomerism and extremism to affect how policymakers think and understand about this technology. However however, we still have to recognize this technology is moving really fast and don't get policy ahead of the technology too quickly. And the risk that we we run as a nation, our greatest source of national security concern with respect to AI is that other countries adopt this technology while we are so angry at it or afraid of it or somehow paranoid of it that our industries, our society don't take advantage of AI. And so I'm just mostly worried about the fusion of AI here in The United States. Chamath Palihapitiya (00:18:57): Can you just double click if you were in the seat in the boardroom of Anthropic over that whole scuttlebutt with the Department of War? It sort of builds on this idea of people didn't know what to think. It's sort of added to this layer of either resentment or fear or just general mistrust that people have sometimes at the software levels of AI, what would you would do you think you would have told Dario and that team to do maybe differently to try to change some of this outcome and some of this perception? Jensen Huang (00:19:25): The first thing that I I would would say about Anthropic is first of all, the technology is incredible. Incredible. We are a large consumer of Anthropic technology. Really admire their focus on security, really admires their focus on safety. The the culture by which they went about it, the the technology excellence by which they went about it, really fantastic. I I would say that that the the desire to warn people about the capability, the technology is is also really terrific. We just have to make sure that we understand that the world has a spectrum and that that warning is good, scaring is less good. Right. And because this technology is too important to us. David Friedberg (00:20:09): Right. Jensen Huang (00:20:10): And and I think that it is fine to predict the future, but we need to be a little bit more circumspect. We need to have a little bit more humility that, in fact, we can't completely predict the future. And to say things that are quite extreme, quite catastrophic, that there's no evidence of it happening, could be more damaging than people think. And of course, we are technology leaders. There were there was a time when nobody listened to us. Yeah. But now because technology is so important in the social fabric, such an important industry, so important to national security, our words do matter. And I think we have to be much more circumspect. We have to be more moderate. We have to be more balanced. We have to be more for more thoughtful. Brad Gerstner (00:20:59): Well, I you know, I would nominate you. I think the industry's gotta get together. 17% popularity of AI in The United States. I mean, we see what happened to nuclear. Right? We basically shut down the entire nuclear industry, and now we have a 100 fission reactors being built in China and zero in The United States. We hear about moratoriums on data centers, so I think we have to be a lot more proactive about that. But I want to go back to this agentic explosion that you're seeing inside your company, the efficiencies, the productivity gains inside your company. There's a lot of debate whether or not we're seeing ROI, Right? And you and I entering into this year, the big question was, are the revenues going to show up? Are the revenues gonna scale like intelligence? And then we had this kind of Oppenheimer moment, a $56,000,000,000 month by Anthropic in February. Do you think, as you look ahead, you announced a trillion dollar you know, visibility into a trillion dollars of just Blackwell and Vera Rubin over the course of the next couple years. When you see this happening at Anthropic and OpenAI, do you think we're on that curve now where we're going to see revenues scale in the way that intelligence is scaling? Jensen Huang (00:22:07): When you look around I'll answer this a couple of different ways. When you look around this audience, you will see that Anthropic and OpenAI is represented here. But in fact, every but 99% of everything that is here is all AI, and it's not Anthropic and OpenAI. Chamath Palihapitiya (00:22:21): Right. Right. Jensen Huang (00:22:21): And the reason for that is because AI is very diverse. I would say that the second most popular model as a category is open models. Number one is yeah. Open open Open ways. Open source. Open AI is number one. Open source is number two. Very distant third is Anthropic, and that tells you something about the scale of all of the AI companies that are here. And so so it's important to recognize recognize that. Let me let me come back and say couple things. One, when we went from generative to reasoning, the amount of computation we needed was about a 100 times. Jason Calacanis (00:23:00): Right. Jensen Huang (00:23:00): When we went from reasoning to agentic, the computation is probably another 100 times. Now we're looking at in just two years, computation went up by a fact 10,000 x. Meanwhile, people pay for information, but people mostly pay for work. Brad Gerstner (00:23:22): Right. Yes. Jensen Huang (00:23:25): Talking to a chatbot and getting an answer is super great. Right. Helping me do some research, unbelievable. But getting work done, I'll pay for. Indeed. Yeah. And so that's where we are. Yeah. AgenTic Systems get work done. They're helping our software engineers get work done. And and so then you take that. You got 10,000 x more compute. You get probably, at this point, a 100 x more consumption now. Chamath Palihapitiya (00:23:49): Yes. Jensen Huang (00:23:49): Yeah. And we haven't even started scaling yet. We are absolutely at a million x. Jason Calacanis (00:23:54): Which is, I think, a great place to talk about the number 10 of trillion marketing people. Twenty, thirty thousand at the company, something Jensen Huang (00:24:00): like We have 43,000 employees. You know, I would say 38,000 are engineers. Jason Calacanis (00:24:06): The conversation we've had on the pod a number of times is, oh my god. Look at the token usage in our companies. It is growing massively. And some people are asking, hey. When I join a company, how many tokens do I get? Because I wanna be an effective employee. And you postulated, I believe, during your two and a half hour keynote, pretty long keynote, well done, that you were spending Jensen Huang (00:24:30): If it was well done, it would be shorter. Yeah. I just Jason Calacanis (00:24:32): wanna let you know. Time to do a Yeah. David Friedberg (00:24:34): It's a Chamath Palihapitiya (00:24:34): They have time to write us an hour Jensen Huang (00:24:36): and forty five. So you guys so you guys know so you guys know, there is no practice. Yeah. And so it's a gripping and ripping. Chamath Palihapitiya (00:24:41): Grip and ripping. Jensen Huang (00:24:42): Yeah. Yeah. I love it. So so I just wanna let you know I was writing the speech while I was giving the speech. Okay? Jason Calacanis (00:24:47): So you never know. But does that mean if we do Jensen Huang (00:24:51): back I apologize. Jason Calacanis (00:24:52): Back a little math. Yeah. 75,000 in tokens for each engineer or something like that? So are you spending in NVIDIA a billion, $2,000,000,000 on tokens for your engineering team right now? Jensen Huang (00:25:02): We're to. Let me give you the thought Let's say you have a software engineer or AI researcher, and you pay them $500,000 a year. We do that all the time. Yeah. Okay? This is happening all of the time. That $500,000 engineer, at the end of the year, I'm gonna ask him how many how much did you spend in tokens? And that person said $5,000. I will go ape something else. Brad Gerstner (00:25:24): Yes. Right. Jensen Huang (00:25:25): If that if that $500,000 engineer did not consume at least $250,000 worth of tokens, I am going to be deeply alarmed. Okay? And this is no different than one of our chip designers who says, guess what? I'm just gonna use paper and pencil. I don't think I'm gonna need any CAD tools. Right. Jason Calacanis (00:25:45): Right. This is a real paradigm shift to start thinking about these all star employees. It almost reminds me of of what we learned in the NBA when LeBron James started spending a million dollars a year just on his health of his body, like, in maintaining it. Jensen Huang (00:25:58): That's right. Jason Calacanis (00:25:58): Here he is at age 41 still playing. It really is, hey. If these are incredible knowledge workers, why wouldn't we give them superhuman abilities? Jensen Huang (00:26:07): That's exactly right. Jason Calacanis (00:26:08): Where does that go? If we if we extrapolate out two or three years from now, what is the efficiency of that all star at NVIDIA and what they're able to accomplish? What does look like? Jensen Huang (00:26:19): Well, first of all, things that that that, wow, this is too hard. That thought is gone. This is gonna take a long time. That thought is gone. We're gonna need a lot of people. That thought is gone. This is no different than in this in the last industrial revolution. Somebody goes, boy, that building really looks heavy. Nobody says that. Chamath Palihapitiya (00:26:39): Right. Jensen Huang (00:26:39): Nobody wow. That mountain looks too big. Nobody says that. Right. Everything that's too big, too heavy, takes too long, those thought those ideas are all gone. Chamath Palihapitiya (00:26:48): They reduce the creativity. Jensen Huang (00:26:50): That's right. Chamath Palihapitiya (00:26:50): What can you come up Jensen Huang (00:26:51): with? Exactly. Right. Which means now the question is, how do you how do you work with these agents? Well, it's just a new way of doing computer programming. In the few in the past, we code. In the future, we're gonna we're gonna write ideas, architectures, specifications. We're gonna organize teams. We're gonna give you we're gonna help them define how to evaluate the definition of good versus bad. Right. What's the what does it look like when something is a great outcome? How to iterate with you? How to brainstorm? That's really what you're looking for, and I'm I think that every engineer is gonna have 100, a 100 agents. Jason Calacanis (00:27:25): Back to the PR problem the industry has right now. You have executives like David Friedberg with AlHalo, who's looking at literally taking through the use of technology, your technology and AI, the number of calories produced and making high quality calories. What is the factor you think you can bring the cost down, Freiburg, and what impact does this vision have David Friedberg (00:27:50): for what you're doing? Zero shot genomic modeling, and it works. Yeah. And then you have that moment, and you're like, holy Honestly, like and and and that's after people are replacing entire enterprise software stacks in a night. I did something in ninety minutes. Was telling the guys about replace the whole software stack and, like, a whole bunch of workload. Ninety minutes on Cloud, ran this agentic system, built the whole thing, deployed it, and we got we were Chamath Palihapitiya (00:28:15): Sunday night. David Friedberg (00:28:15): On a Sunday night, 10PM. I was done at 11:30. I went to bed. Jason Calacanis (00:28:18): As the CEO, you replaced Yeah. David Friedberg (00:28:21): And everyone on my management team had to do a similar exercise over the weekend. What we saw on Monday, I was like, it's over. But the technical stuff, the science stuff, we did something in thirty minutes using auto research, and I'd love your view on auto research and what that tells us about how far we still have to go in terms of efficiency. But using auto research and a chunk of data, something was published internally that we said, oh my god. And that would normally be a PhD thesis that would take seven years. That would be one of the most celebrated PhD theses we've ever seen in this field, and it would be in the journal Science. And it was done in thirty minutes on a desktop computer running on Auto Research with all the data we just ingested. We got it on Friday. We're like, hey. Let's try it. Try boot it up, going to GitHub, downloaded Auto Research, and ran it. And you see everyone's face just go like and then the potential of what this is unlocking for us is like the kind of thing that would take seven years, and it happened in thirty minutes. And we're experiencing it in genomics, and we're like, this is unbelievable. So I I think, like, the acceleration is widening the aperture for everyone in a way that like, you didn't imagine a few years ago. But just going back to the auto research point, can you just comment on what you think about the fact that this thing got published with 600 lines of code in a weekend and the capacity that it has to run locally and achieve what it can achieve with all of these diverse datasets and what that tells us about the early stages we are in terms of optimization on algorithms and hardware. Jensen Huang (00:29:46): The fundamental reason why OpenCLaw is so incredible, number one, is its its confluence, its timing with the breakthroughs in large language model. Yeah. Its timing was perfect. It was impeccable. David Friedberg (00:30:00): Yeah. Jensen Huang (00:30:00): Now in a lot of ways, Peter would have come up with it probably if not for the fact that Claude and GPT and ChadGPT have reached a level that is really very good. Chamath Palihapitiya (00:30:10): Right. Jensen Huang (00:30:10): It is also a new capability that allows these models to tool use. The tools that we've created over time, web browsers and Excel spreadsheets, you know, in the case of chip design, Synopsys and Cadence and Omniverse and Blender and Autodesk, all of these tools are gonna continue to be used. There's some some people say that that the enterprise IT software industry is gonna get destroyed. There's it's there's a let me give you the alternative view. The enterprise software industry is limited by butts and seats. It's about to get a 100 times more agents banging on those tools. There are gonna be agents banging on SQL. There are gonna be agents banging on vector databases, banging on Blender, agents banging on Photoshop, and the reason for that is because those tools are, first of all, do a very good job. Second, those tools are the conduit between us. In the final analysis, when the work is done, it has to be represented back to me in a way that I can control. Right. And I know how to control those tools. And so I need everything to be put back into Synopsys. I want everything to be put back into Cadence because that's how I control it. That's how I've ground truth. Chamath Palihapitiya (00:31:24): Let me ask you a question about open source. So we have these closed source models. They're excellent. Yeah. We have these open weight models. Many of the Chinese models are incredible, absolutely incredible. Two days ago, you may not have seen this because you were busy on stage, but there was a training run that happened in this crypto project called BitTensor. Subnet three, they managed to train a 4,000,000,000 parameter LAMA model, totally distributed, with a bunch of people contributing excess compute, but they were able to do it statefully and manage a training run, which I thought was like a pretty crazy technical accomplishment Yeah. Because it's like random people and Yeah. Each person gets a little chair. Jensen Huang (00:32:04): Our our modern version of folding at home. Exactly. Chamath Palihapitiya (00:32:07): Yeah. So what what do you think about the end state of open source? Do you see this decentralization of architecture as well and decentralization of compute to support open weights and a totally open source approach to making sure AI is broadly available to everyone? Jensen Huang (00:32:22): I believe we fundamentally need models as a first class product, proprietary product, as well as models as open source. These two things are not a or b, it's a and b. There's no question about it. And the reason for that is because models is a technology, not a product. Model is a technology, not a service. For the vast majority of consumers, the horizontal layer, the general intelligence, I would really, really love not to go fine tune my own. Chamath Palihapitiya (00:32:54): Right. Jensen Huang (00:32:54): I would really love to keep using ChatGPT. I love to use Cloud. I love to use Gemini. I love to use X. And they all have their own personalities, as you know, which kind of depends on my mood and depends on what problem I'm trying to solve. I might do it on X, or I might do it on ChatGPT. And so that segment of the industry is thriving. It's going to be great. However, there all these industries, their domain expertise, their specialization has to be channeled, has to be captured in a way that they can control, and that it can only come from open models. The open model industry, we're contributing tremendously to. It is near the frontier. And quite frankly, even if it reaches the frontier, I think that products as a service, world class products as as a pro models as a product is gonna continue to thrive. Jason Calacanis (00:33:46): Every start up we're investing in now is open source first and then going to the proprietary models. Jensen Huang (00:33:53): Yeah. And the beautiful thing is because you have a great router, you connect it to, on first day, every single day, you're gonna have access to the world's best model, and and then it gives you time to cost reduce and fine tune and specialize. And so you're gonna have world class capabilities out to shoot every single time. Brad Gerstner (00:34:12): Gents, can I Of ask you course? Nobody wants The US to win the global AI race more than you, right? But a year ago, the Biden era diffusion rule really was an anti American diffusion of AI around the world. So, we are a year into the new administration. Give us a grade. Where are we in terms of global diffusion and the rate at which we're spreading US AI technology around the world? Are we an a? Are we a b? Are we a c? What what's working? What's not working? Jensen Huang (00:34:44): Well, first of all, president Trump wants American industry to lead. He wants American technology industry to lead. He wants American technology industry to win. He wants us to spread American technology around the world. He wants United States to be the wealthiest country in the world. He wants all of that. At the current moment as we speak, NVIDIA gave up a 95% market share in the second largest market in the world, and we're at 0%. President That's right. President Trump wants us to get back in there, and and, the first thing is, to get license licensed for the companies that we're gonna be able to sell to. We've got many companies who have requested for licenses. We've applied for licenses for them, and we've got approved licenses from secretary Hutt Ludnick. Now we've we've informed the Chinese companies, and many of them have given us purchase orders. And so we're gonna we're gonna we're in the process of cranking up our supply chain again to go ship. I think at the highest level, Brad, I think one of the things that we should acknowledge is this. Our national security is diminished when we don't have access to miniature motors, rare earth minerals. It's diminished when we don't control our telecommunications networks. It's diminished when we can't provide for sustainable energy for our country. It is fundamentally diminished. Every single one of these industries is an example of what I don't want the AI industry to Chamath Palihapitiya (00:36:20): be. Right. Jensen Huang (00:36:21): When we look forward in time and we say, what do we want? What is the what does it look like when American technology industry, American AI industry leads the world, we can all acknowledge that there is no way that AI models is one universally. It is we can all acknowledge that that is an outcome that makes no sense. However, we can all imagine that the American tech stack from chips to computing systems to the platforms are used broadly by the world where they build their own AI. They use public AI. They use private AI, whatever, and they can build their applications in their society. I would love that the American tech stack is 90% of the world. Yes. I would love that. The alternative, if it looks like solar, rare earth, magnets, motors, telecommunications, I consider that a very bad outcome for national security. Chamath Palihapitiya (00:37:21): Agreed. Yeah. Yep. David Friedberg (00:37:22): How much are you monitoring the situation with the conflicts around the world right now, and how much does it worry you, Jensen? So China and Taiwan and then helium availability coming out of The Middle East, I understand can be a supply chain risk to semiconductor manufacturing. How much do these situations worry you? How much are you spending on them? Jensen Huang (00:37:40): Well, first of all, I think the in Middle East, I have we have 6,000 families there. Yeah. We have a lot of Iranians, at NVIDIA, and their families are still in Iran. And so so we have we have a lot of families there. The first thing is is they're quite anxious. They're quite concerned, quite scared. We're thinking about them all the time. We're monitor and keeping an eye on them all the time. They have a 100% of our support. I've been asked several times, are we still considering being in Israel? We are 100% in Israel. We are a 100% behind the families there. We are a 100% in The Middle East. I was also asked, you know, given what's happening in The Middle East, is that an area where we believe that we can expand artificial intelligence to? I believe that there there's a reason we went to war, and I believe at the end of the war, Middle East will be more stable than before. And so if we were there if we're considering it before, we should absolutely be considering it after. And so I'm a 100% in on that. With respect to with with with with with respect to to Taiwan, we have to do three things. One, we have to make sure that we re industrialize The United States as fast as we can. Yeah. And whether it's the chip manufacturing plants, the the computer manufacturing plants, or the AI factories Jason Calacanis (00:38:56): doing on that? Jensen Huang (00:38:57): We're doing excellent. With by by gaining the strategic support, by gaining the friendship of the supply chain of Taiwan, by gaining their friendship, by gaining their support, we were able to build Arizona and Texas, California at incredible rates. They're they are genuinely a strategic partner. We we we really they deserve our support. They deserve our friendship. They deserve our, generosity, and they're doing everything they can to accelerate the manufacturing process for us. And so so I think that's number one. Number two, we ought to diversify the manufacturing supply chain. And whether it's South Korea, whether it's Japan, it's Europe, we to diversify the supply chain, make it more resilient. And number three, let's demonstrate restraint. And while we're reducing increasing our diversity and resilience, let's not press, push. Jason Calacanis (00:40:03): You know? Unnecessary. We need to be patient. David Friedberg (00:40:05): It's it's thoughtful. Is helium a problem? A lot of reports Jensen Huang (00:40:08): You know, I I think helium could be a problem, but it's also the case that the supply chain probably has a lot of buffer in it. Yeah. These kind of things tend to have a lot of buffer, but but you know? Yeah. Jason Calacanis (00:40:19): You've made massive progress in self driving. You made a big announcement. You've added many more partners, including BYD. There was just a video of you driving around in a Mercedes, and a huge announcement with Uber that you're gonna have a number of cars on the road from many different manufacturers. Your bet is, I believe, that there's going to be an Android type open source platform that you're gonna play a major part in with dozens of car providers, and then maybe on the other side, there could be an iOS with Tesla or Waymo. What's your strategy thinking there and how that chessboard emerges? Because it feels like you have a a pretty deep stack, and in some ways, you're competing, and in other places, you're collaborative. Jensen Huang (00:41:08): Yeah. It's taking a step back, we believe that everything that moves will be autonomous completely or partly someday, number one. Number two, we don't wanna build self driving cars, but we wanna enable every car company in the world to build self driving cars. And so we built all three computers, the training computer, the simulation computer, the evaluation computer, as well as the car computer. We developed the world's safest driving operating system. We also created the world's first reasoning autonomous vehicle so that it could decompose complicated scenarios into simpler scenarios that it knows how to navigate through, just like us, reasoning systems. And so that reasoning system, called Alpomayo, has enabled us to achieve incredible results. We open this we we vertical optimization. We horizontally innovate, and we let everybody decide. Do you wanna buy one computer from us? In the case of Elon and Tesla, they buy our training computers. Do they wanna buy our training computer and our simulation computers? Or do you wanna let us work with us to do all three and even put the car computer in your car? So we you know, our attitude is we wanna solve the problem. We're not the solution provider, and we're delighted however you work with us. Chamath Palihapitiya (00:42:32): Let me build on this question because I think it's, like, it's so fascinating. You actually do create this platform. A thousand flowers are blooming, but it's also true that some of those flowers wanna now go back down in the stack and try to compete with you a little bit. Google has TPU. Amazon has Inferentia and Tranium. You know, everybody's sort of spinning up their own version of, I think I can out NVIDIA NVIDIA, even though they also tend to be huge customers. Yeah. How do you navigate that? And Yeah. What do you think happens over time, and where do those things play in the complexion of this kind of vision? Yeah. Jensen Huang (00:43:06): Really great. You know, first of all, we're the only AI company we're an AI company. We build foundation models. We're at the frontier in many different domains. We build every single every single layer, every single stack. We're the only AI company in the world that works with every AI company in the world. They never show me what they're building, and I always show them exactly what I'm building. Chamath Palihapitiya (00:43:28): Right. Jensen Huang (00:43:28): Yeah. And so so the the confidence comes from this. One, we are delighted to compete on what is the best technology. And to the extent that to the extent that we can continue to run fast, I believe that buying from NVIDIA still is one of the most economic things they could do, and I just have incredible confidence there. Number one. Number two, we're the only architecture that could be in every cloud, and that gives us some fundamental advantages. We're the only architecture you could take from a cloud and put into on prem, in the car, in any region. Jason Calacanis (00:44:00): In space. Jensen Huang (00:44:01): That's right. In space. And so there's a whole whole part of our market, about 40% of our of our business. Most people don't realize this. 40% of our business, unless you have the CUDA stack, unless you can build an entire AI factory, you have the customers don't know what to do with you. They're not trying to build chips. They're not trying to buy chips. They're trying to build AI infrastructure, and so they want you to come in with a full stack, and we've got the whole stack. And so surprisingly, NVIDIA's gaining market share. If you look at where we are today, we're gaining share. Chamath Palihapitiya (00:44:32): But you think what happens is these guys try and they realize, oh my god. It's too much, and then they come back. Is that why this share grows? Jensen Huang (00:44:38): Well, we're gaining share for several reasons. One, our velocity has gone we help people realize it's not about building the chip. It's about building the system. Chamath Palihapitiya (00:44:47): System is. Jensen Huang (00:44:48): And that system's really hard to build. And and so their their their business with us is increasing. In the case of AWS, I think they just announced, I think it was yesterday, that they're gonna buy a a million chips in the next couple years. I mean, that's a lot of chips from from AWS, and that's on top of all the chips they've already bought. And so we're delighted to do that. But number one, we're gaining share this last couple of years because we now have Anthropic coming to NVIDIA. Meta SL is coming to NVIDIA, and the growth of open models is incredible, and that's all on NVIDIA. And so we're growing in share because of the number of models. We're also growing in share because outs all of these companies are outside the cloud, and they're growing regionally in enterprise, in industries, at the edge, and that entire segment of growth is really hard to do if it's just building an ASIC. Brad Gerstner (00:45:43): Brad, related to that, and not to get in the weeds on the numbers, but analysts don't seem to believe. Right? So if you look at the consensus forecast, you said compute could 1,000,000 x. Right? And yet, they have you growing next year at 30%, the year after that at 20%, and in 2029, which is supposed to be a monster year, at 7%. Right? So if you just if you take your TAM and you apply their growth numbers, it suggests that your share will plummet. Do you see anything in your future order book that would make that correct? Jensen Huang (00:46:18): Yeah. First of all, they just don't understand the scale and the breadth of AI. Yes. Chamath Palihapitiya (00:46:24): Yeah. Yeah. Think that's true. Jensen Huang (00:46:25): Most people think that AI is in the top five hyperscalers. Chamath Palihapitiya (00:46:29): Right. That's right. Also an orthodoxy around these law of large numbers where, you know, they have to go back to their investment banking risk committee and show some model. They're not gonna believe in their minds that 5,000,000,000,000 goes to 15,000,000,000,000. David Friedberg (00:46:43): It's a it's Jason Calacanis (00:46:44): a of the doubt. Chamath Palihapitiya (00:46:45): It can go to 7. Never mind. David Friedberg (00:46:46): And you Brad Gerstner (00:46:47): can have a $10,000,000,000,000 company. Chamath Palihapitiya (00:46:48): It's all just CYA stuff It's that I think David Friedberg (00:46:50): never happened before, so you can't say Jensen Huang (00:46:52): it will. And and because because you have to redefine what it is that you do. There was somebody who made an observation recently that NVIDIA how can you be larger than Intel in servers? And the reason for that is because the CPU market of the entire data center was about $25,000,000,000 a year. Chamath Palihapitiya (00:47:12): Right. Right. Jensen Huang (00:47:12): We do $25,000,000,000 a year, as you guys know, in a very in a time that we were sitting here. And so, obviously, obviously, that was a joke. No. It's but Chamath Palihapitiya (00:47:23): it's All Jensen Huang (00:47:24): in podcast. David Friedberg (00:47:24): It's really true. Jensen Huang (00:47:25): It is. David Friedberg (00:47:25): Don't worry. Everything on this show is rough. Don't Jason Calacanis (00:47:27): worry, Hannah. Chamath Palihapitiya (00:47:28): It's all in. You can keep I'll keep I'll David Friedberg (00:47:30): I mean, god, thank you. Jensen Huang (00:47:30): That was guidance. But anyhow anyhow, it the the point is how big you can be Yeah. Depends on what is it that you make. Brad Gerstner (00:47:39): Right. Jensen Huang (00:47:39): NVIDIA is not making chips. Number one, making chips does not help you solve the AI infrastructure problem anymore. It's too complicated. Number three, most people think that AI is narrowly in the things that they talk about and hear and see. Right. AI is much OpenAI is incredible. They're gonna be enormous. Anthropic is incredible. They're gonna be enormous. But AI is going to be much, much bigger than that. Yeah. Jason Calacanis (00:48:05): Tell us Jensen Huang (00:48:05): about And we address that segment. Chamath Palihapitiya (00:48:06): Tell us about data centers in space for a second. Jensen Huang (00:48:09): Yep. We're already in space. Chamath Palihapitiya (00:48:11): How should the layman think about what that business is versus when you hear about these big data center build outs that's happening in in on the ground? Jensen Huang (00:48:21): Well, we should definitely work on the ground first because we're already here, and number one. Number two, we should prepare to be out in space, and, obviously, there's a lot of energy in space. The challenge, of course, is that cooling, you can't take advantage of con con conduction David Friedberg (00:48:36): Exactly. And Jensen Huang (00:48:37): And so you can only use radiation, and radiation requires very large surfaces. And so Right. Now that's not an impossible thing to solve, and there's a lot of lot of space in space. But nonetheless, the expense is still quite there is is there. We're gonna go explore it. We're already there. We're already radiation hardened. We have we have CUDA in satellites around the world. They're doing imaging, image processing, AI imaging, and and that kind of stuff ought to be done in space instead of sending all the data back here and do imaging down here. We ought to just do imaging out in space. And so there's a lot of things that we ought to do in space. In the meantime, we're gonna explore what does the architecture of data centers look like in space. And it'll take it'll take years. It's okay. I want to do I got plenty of time. Jason Calacanis (00:49:23): I want to double click on health care. I know you've got a big effort there. We're all of a certain age where we're thinking about lifespan, health span. I mean, we all look great, I think. Chamath Palihapitiya (00:49:33): Some better than others. Jensen Huang (00:49:34): I think Jason Calacanis (00:49:34): some better than others. I don't know what your secret is, Jensen. We're pretty Chamath Palihapitiya (00:49:37): good these these Jason Calacanis (00:49:38): I mean, what's what are you taking? What's off the menu? You gotta talk to me when we're backstage. I wanna know in the green room what you got going on. Jensen Huang (00:49:45): Squats and push ups and sit ups. Perfect. Jason Calacanis (00:49:47): Yeah. But Brad Gerstner (00:49:50): That works. Jason Calacanis (00:49:50): What you know in terms of the build out in health care, where is that going, and what kind of progress are we making? I was just using Claude to do some analysis and saying, like, where are all these billing codes? We spend twice as much money in The US. We get seem to get half as much. It seemed like 15 to 25% of the dollar spent were on these first GP visits. And I think we all know, like, ChatGPT and large language model does a better job more consistently today at a first visit. So what has to happen there to kind of break through all that regulation and have AI have a true impact on the health care system? Jensen Huang (00:50:29): There's several several areas that we're involved in in, in health care. One is, AI, physics, and and that's or AI biology. Using AI to understand, represent, predict biology behavior, biological behavior. And so that's one. That's very important in drug discovery. There's second, which is AI agents, and that's where the assistance in helping diagnosis and things like that. Open Evidence is a really good example. Hippocratics is a really good example. Love working with those companies. I really think that this is an area where agentic technology is gonna revolutionize how we interact with doctors and how do we interact for healthcare. The third part that we're in involved in is physical AI. The first one's AI physics, using AI to predict physics. The second one is physical AI, AI that understand the properties of the laws of physics, and that's used for a robotic surgery, Huge amounts of activities there. Every single instrument, whether it's ultrasound or, you know, CT or whatever instrument we interact with in a hospital in the future will be agentic. Yeah. You know? Open claw in a safe version Chamath Palihapitiya (00:51:39): Yeah. Jensen Huang (00:51:40): Will be inside every single instrument. And so in a lot of ways, that instrument's gonna be interacting with patients and nurses and doctors in Yeah. Jason Calacanis (00:51:47): We've a very unique so much investment in AI weapons. Would be wonderful to see some investment in AI EMTs and paramedics and saving lives, not just taking them, which I think is a great segue into robotics. You've got dozens of partners. We have this very weird, I I don't know. I wanna call it lost decade or twenty years of Boston Dynamics. Google bought a bunch of companies. They then wound up selling them and spitting them out, where people just thought, robotics is just not ready for prime time. Now here we have the world's greatest entrepreneur at this time tied with you, Elon Musk doing well, that was a good save, I hope. Optimus, pretty impressive. Then other companies in China, how close is that to actually being in our lives where we might see a chef, a robotic chef, a robotic nurse, a robotic housekeeper, this humanoid factor actually working in the real world, knowing what you know with those partners and the fidelity, especially in China where they seem to be doing as good a job as we're doing here or maybe better? Mhmm. Jensen Huang (00:52:52): We invented the industry largely. America invented we you could argue we got into it too soon. Yeah. And and we got exhausted. We got tired, about five years before the enabling technology appeared. Yeah. Jason Calacanis (00:53:09): It's the brain. Jensen Huang (00:53:09): Yeah. Yeah. And we we just got tired of it just a little too soon. Okay? That's number one. But it's here now. Now the question is how much longer? From the point of high functioning existence proof, high functioning existence existence proof to reasonable products, technology never takes more than a couple, two, three cycles. And so a couple, two, three cycles would basically be somewhere around three years to five years. That's it. Three years to five years, we're gonna have robots all over the place. I think I think China is is formidable, and the reason for that is because their microelectronics, their motors, their rare earth, their magnets, which is foundational to robotics, they are the world's best. And so in a lot of ways, our robotics industry relies deeply on their ecosystem and their supply chain. And and they're, you know, obviously moving very quickly. We're gonna you know, our robotics industry will have to rely a lot on it. The world's robotics industry will have to rely on a lot on it. And so so I think, you're gonna see some fast fast movements here. Jason Calacanis (00:54:16): Ultimately, one for one. Elon seems to think we're gonna have one robot for every human, 7,000,000,000 for 7,000,000,000, 8,000,000,000 for 8,000,000,000? Jensen Huang (00:54:23): Well, I'm hoping more. Yeah. I'm hoping more. Yeah. Well, first of all, there's a whole bunch of robots that are gonna be in factories working around the clock. There's gonna be a whole bunch of factory robots that that don't move. They move a little bit. Almost everything will be robotic. David Friedberg (00:54:38): What is the world look like? Sorry. Let me say I think, like, this Jensen Huang (00:54:41): is one of the David Friedberg (00:54:41): the robotics for me is one of the pieces that I think unlocks economic mobility opportunities for every individual. Everyone now like, when everyone got a car, they could now go and do a lot of different jobs. When everyone gets a robot, their robot can do a lot of work for them. They can stand up an Etsy store or a Shopify store. They can create anything they want with their robot. They could do things that they independently cannot do. I think the robot is gonna end up being the greatest unlock for prosperity for more people on Earth than we've ever seen with any technology before. Jensen Huang (00:55:13): Yeah. No doubt. I mean, just the simp the the the simple math at the moment is we're millions of people short in labor today. Brad Gerstner (00:55:19): Right? Jensen Huang (00:55:19): Yeah. Right? We're we're we're actually really desperate in need of robotics. And so that all of these companies could grow more if they had more labor. I mean, we're we're number one. Some of the things that you've mentioned are super fun. I mean, because of robots, we'll have virtual presence. You know, I'll be able to go into the robot at my house and virtually operate it. I'm on a business trip. Right. Chamath Palihapitiya (00:55:45): Tell me I'll Jensen Huang (00:55:46): be Walk around the house. Jason Calacanis (00:55:47): And walk the dog. Yeah. Walk the dog. Break the leaves. Jensen Huang (00:55:49): Yeah. Exactly. Break out the dog. Maybe not quite that, but just, you know, just, you know, wander around Yeah. And just see what's going on in the house. You know, chat with the dogs. Chat with the kids. Yeah. Brad Gerstner (00:55:59): Yeah. And time Jensen Huang (00:56:00): travel is also we're gonna be able to travel at the speed of light. You know? And so, you know, clearly, we're gonna send our robots ahead of us. Yeah. Not gonna send myself. I'm gonna send a robot. Chamath Palihapitiya (00:56:10): Right. You know? Check it out. Jensen Huang (00:56:11): Yeah. Yeah. And then I'm gonna upload my AI. David Friedberg (00:56:13): Well, it's inevitable. It unlocks the moon, and it unlocks Mars as targets for for colonization, which gives us infinite resources. Getting back from the moon is effectively zero energy cost to move material back because you can use solar and accelerate. So you could have factories that make everything the world needs on the moon, and the robots are gonna be the unlock for enabling Jensen Huang (00:56:32): That's right. Distance no longer David Friedberg (00:56:34): doesn't matter. Yep. Yeah. Brad Gerstner (00:56:35): The more revenue we get out of models and agents, the more we can invest in building the infrastructure, which then unlocks more capabilities on models and agents. Dario on Dwarkesh's podcast recently said, By '27, 'twenty eight, we'll have hundreds of billions of dollars of revenue out of the model companies and the agent companies, and he forecasts a trillion dollars by 2030, right? This is non infrastructure AI revenue. Jensen Huang (00:57:01): I think he I think he's he's being very conservative. I believe Dario and Anthropic is gonna do way better than that. Wow. Jason Calacanis (00:57:08): Way better Brad Gerstner (00:57:09): than that. Wow. Jason Calacanis (00:57:09): So 30,000,000,000 to a trillion. Jensen Huang (00:57:11): Yep. And not and and the reason for that is the one part that he hasn't considered is that I believe every single enterprise software company will also be a reseller value added reseller of Anthropic code Anthropix tokens. Value added reseller to be. O OpenAI. Have to be. Value that's right. Jason Calacanis (00:57:30): Yeah. Jensen Huang (00:57:31): And they're gonna that that that part of their Chamath Palihapitiya (00:57:34): Get this logarithmic expansion. Jensen Huang (00:57:35): Yes. Chamath Palihapitiya (00:57:36): Yeah. Jensen Huang (00:57:36): Their go to market is going to expand tremendously Chamath Palihapitiya (00:57:40): What do Jason Calacanis (00:57:40): you think? Chamath Palihapitiya (00:57:40): This year. What do you think in that world is the moat? What's left over? I mean, you have some moats that are, frankly, I think, as this scales, almost insurmountable. The best one that nobody talks about is probably CUDA, which is just, like, an incredible strategic advantage. But in the future, if a model can be used to create something incredible, then the next spin of a model can be used to maybe disrupt it. Sort of in your mind, what do you think for these companies that are building at that application layer? What's their moat? Like, how do they differentiate themselves? Jensen Huang (00:58:12): Deep specialization. Deep specialization. I believe that these models they're gonna have general models that are connected into the software company's agentic system. Right. Many of those models are cloud models and proprietary models, but many of those models are specialized sub agents that they've trained on their own Right. So the Chamath Palihapitiya (00:58:38): call to arms for you, for entrepreneurs, is, look, Know your vertical. Jensen Huang (00:58:41): That's right. Chamath Palihapitiya (00:58:42): Know it as deep and as better than everybody else. Jensen Huang (00:58:45): That's right. Chamath Palihapitiya (00:58:45): And then wait for these tools because they're catching up to you, and now you can imbue it with your knowledge. Jensen Huang (00:58:49): That's right. The sooner you connect your agent the sooner you connect your agent with customers, that flywheel is gonna cause your agent Chamath Palihapitiya (00:58:57): to get It very it very much is an inversion of what we do today because today, we build a piece of software, we say, what generalizes? And then let's try to sell it as broadly as possible and then sell the customization around Brad Gerstner (00:59:08): it. And we trap David Friedberg (00:59:09): And in fact Jensen Huang (00:59:09): in fact, exactly right. We we create a horizontal, but notice there are all these GSIs and all of these Yeah. Consultants Exactly. Who are specialists Jason Calacanis (00:59:19): Yes. Jensen Huang (00:59:20): Who then take your horizontal platform and specializes it into. Exactly. Jason Calacanis (00:59:24): And that's arguably a five or six time bigger industry is the customization. Jensen Huang (00:59:27): It is. Absolutely. The whole Chamath Palihapitiya (00:59:29): of that out. Very much is. Jensen Huang (00:59:30): That's right. So I think that these platform companies have an opportunity to become that specialist, to become that vertical. Domain expert. Jason Calacanis (00:59:38): I just want to give you your flowers. I think it was three years ago you said, you're not going to lose your job to AI. You're going to lose your job to somebody using AI. And here we are. The entire conversation has revolved around this concept of agents making people superhuman and the business opportunity expanding and entrepreneurship expanding. You actually saw it pretty clearly. Yeah. Jensen Huang (00:59:58): That's right. David Friedberg (00:59:59): Changed your view? Because you you you were Chamath Palihapitiya (01:00:01): This is do doomer. Doomer. I'm not doomer. I do have Don't Jason Calacanis (01:00:05): have doomer. No. I I Yeah. You can hold space for, I think, two ideas. One is there are gonna be a Chamath Palihapitiya (01:00:09): That spiral Jake out with me. No. There you can Jensen Huang (01:00:12): see just because he doesn't hang out with me. You know? Jason Calacanis (01:00:15): Well, we I mean, we've hung a little bit. David Friedberg (01:00:18): He will show you out of the table. Jensen Huang (01:00:20): He'll follow you around. I'm not asking for it. I'm just following you around. Jason Calacanis (01:00:23): I mean I'm Jensen Huang (01:00:23): not asking Brad Gerstner (01:00:24): for it. Jason Calacanis (01:00:24): You can come with me and Tucker. We ski in Japan every January. Really? Love it. Me and Tucker will go road trip. Wow. Okay. There is gonna be job displacement. And then the question becomes, you know, do those people have the fortitude, the resolve to then go embrace these, you know, technologies? We're we're going to see a 100% of driving go away by humans. That's just that's a beautiful thing and the lives saved, but we have to recognize that's 15,000,000 people in The United States, 10 to 15,000,000, who are employed in that way. And and so that is gonna happen. Yes? Jensen Huang (01:00:57): I I think I think that jobs will change. For example, there are many chauffeurs today who drives the car. I believe that though many of those chauffeurs will actually be in the car sitting behind the drive the steering wheel while the car is driving by itself. And the reason for that is because remember what a chauffeur does. In the end, these chauffeurs, they're helping you they're your assistants. They're helping you Yes. With your luggage. They're helping you I mean, they're helping you with a lot of things. And and so I wouldn't be surprised, actually, if the chauffeurs of the future becomes your mobility assistant, and they are helping you do a whole bunch of Jason Calacanis (01:01:33): other to the hotel. Yeah. And and the Jensen Huang (01:01:35): car's driving by itself. Chamath Palihapitiya (01:01:36): Right. And the Brad Gerstner (01:01:36): The autopilot in planes created a lot more pilots and didn't take any of the pilots out of the cockpit. Jensen Huang (01:01:42): Yeah. Okay. Brad Gerstner (01:01:43): Even though the autopilot is flying the plane 90% of David Friedberg (01:01:45): the time. Jensen Huang (01:01:46): Way, while that David Friedberg (01:01:46): car is driving itself, that chauffeur is gonna be doing a bunch of other work on his phone, and he's gonna be Arranging, Jensen Huang (01:01:52): for example Do another coordinating a bunch of things for you, getting you Chamath Palihapitiya (01:01:55): know? David Friedberg (01:01:56): Yeah. It's all the pie just grows in a way that Jason Calacanis (01:01:58): You so so Jensen Huang (01:01:58): one of the things that that that yes. Every job will be will be transformed. Some jobs will be eliminated. However, we also know that many, many jobs will be will be created. Yep. The one thing that I will say to young people who are coming out of school who are concerned who are anxious about AI, be the expert of using AI. Jason Calacanis (01:02:17): Yes. Jensen Huang (01:02:18): How much look. Yeah. We all want our employees to be expert at using AI, and it's not not not trivial. Not trivial. And so knowing how to specify, to overprescribe, leaving enough room for the AI to innovate and create while we guide it to the outcome we want. If that all of that requires artistry. Chamath Palihapitiya (01:02:41): You had you had this great advice to when you were at Stanford, I think it was, which is I wish to you pain and suffering. Do you remember that? Yeah. Fantastic. What's your advice to young people around what they should be studying? So if they're sort of about to leave high school because now those are the kids that are at this, like, really native they haven't made a decision about college, what to study, if at all go to college. How do you guide those kids? What would you tell them? Jensen Huang (01:03:06): I I still believe that deep science, deep math, language skills you know, as you know, language is the programming language of Chamath Palihapitiya (01:03:16): AI The ultimate programming language. Jensen Huang (01:03:18): And so as it turns out, it could be that the English major could be the most successful. Yeah. And so I think I would just advise whatever education you get, just make sure that you're deeply, deeply expert in using AIs. One of the things that I wanted to say with respect to jobs, and I want everybody to hear it, that in fact, at the beginning of the deep learning revolution, one of the finest computer scientists in the world, deeply, deeply I deeply deeply respect, predicted that computer vision will completely eliminate radiologists. And that the one field he advises everybody to not go into is radiology. Ten years later, his prediction was at a 100% right. Computer vision has been integrated into all of the radiology technologies and radiology platforms in the world a 100%. The surprising outcome is the number of radiologists actually went up, and the demand for radiologists has skyrocketed. The reason for that is because everybody's job has a purpose and its task. The task that you do is studying the scans, but your purpose is to die helping the doctors, helping the patient diagnose disease. Jason Calacanis (01:04:38): Right. Jensen Huang (01:04:38): And so what's surprising is because the scans are now being done so quickly, they could do more scans, improving health care. Yes. But doing more scans more quickly allows patients to be onboarded a lot more Treated. Yeah. Treated a lot more quickly. Jason Calacanis (01:04:56): Yeah. Jensen Huang (01:04:56): And as it turns out, because hospitals enjoy making money too Jason Calacanis (01:05:00): Yeah. Jensen Huang (01:05:01): Right. They're doing more scans. Treating more patients. More patients. Jason Calacanis (01:05:06): Becomes The Jensen Huang (01:05:06): revenues go up. David Friedberg (01:05:07): Perfect. And guess what? Perfect. Brad Gerstner (01:05:09): Yeah. And and and and a country that grows faster, productivity increases, a wealthier country can put more teachers in the classroom, not less teachers in the classroom. Jensen Huang (01:05:18): That's right. Brad Gerstner (01:05:18): You just give every one of those teachers a personalized curriculum for every student in the room. It makes them all bionic and leads to a lot more. Jensen Huang (01:05:25): Every single student will be assisted by AI, but every single student will need great teachers. Yeah. Jason Calacanis (01:05:31): Yeah. Amazing. Jensen, congratulations on all your success, and, really, this is an incredibly positive, uplifting discussion. We really appreciate you taking the time for us. Chamath Palihapitiya (01:05:40): He is the steward we need. You are. You are the steward we more vocal Jason Calacanis (01:05:44): about the positive side of it. Think there's so much humorism. Chamath Palihapitiya (01:05:49): But I also think it takes the humility to have this level of success and be humble about, we're making software, guys. Yeah. And I think that that's actually really healthy for people to hear. We have done this before. We have invented categories and industries before. Yes. We don't need to go to this scaremongering place. Jason Calacanis (01:06:07): It does nothing. And we get to choose. Right? We have autonomy and and agency. We get to pick how to Jensen Huang (01:06:13): We sure do. Jason Calacanis (01:06:13): Deploy this. Okay, everybody. We'll see you next time Thank you. On the all in interview. Okay? Well done, brother. Chamath Palihapitiya (01:06:19): Thanks, man. Jensen Huang (01:06:20): Good job. Jason Calacanis (01:06:21): Thank you, sir. That was awesome. Good. Good. Appreciate you. Jensen Huang (01:06:24): You guys are awesome. David Friedberg (01:06:25): Jensen, thank you. Jensen Huang (01:06:25): Look at this. Look at this big crowd behind you guys. Jason Calacanis (01:06:27): Man, I think they're here for you.